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JACIII Vol.18 No.6 pp. 918-925
doi: 10.20965/jaciii.2014.p0918
(2014)

Paper:

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Rule Representation for Nursing-Care Process Evaluation Using Decision Tree Techniques

Manabu Nii*,**, Kazunobu Takahama*, Shota Miyake*,
Atsuko Uchinuno***, and Reiko Sakashita***

*Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2201, Japan

**WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan

***College of Nursing Art and Science, University of Hyogo, 13-71 Kitaoji-cho, Akashi, Hyogo 673-8588, Japan

Received:
May 3, 2014
Accepted:
July 16, 2014
Published:
November 20, 2014
Creative Commons License
Keywords:
nursing-care, text classification, dependency relations, phrase, decision tree
Abstract
Improving the quality of nursing care is crucial to maintaining the quality of life. Our objective is to develop a computer-aided evaluation system that enables nursing experts to improve the quality of nursing care. In our previous works, some classification systems based on fuzzy logic, neural networks, and SVMs were developed. Although a classification system with high performance for all nursing-care datasets is desirable, we focus on how to visualize the classification results in this paper. It is important to visualize the results for our nursing-care text classification system because the computer-aided system has to explain the reasons for obtaining such results to human experts. In this paper, a tree-type expression is considered for visualizing the classification results. To visualize classification results with the tree-type expression, we consider a decision tree technique. Word existence, dependency relations, and phrase-based feature vector definitions have been proposed in our previous works. In the present study, these three types of feature vector definitions are compared with one another from the viewpoint of understandability.
Cite this article as:
M. Nii, K. Takahama, S. Miyake, A. Uchinuno, and R. Sakashita, “Rule Representation for Nursing-Care Process Evaluation Using Decision Tree Techniques,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.6, pp. 918-925, 2014.
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References
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Last updated on Oct. 01, 2024